code: def _train_tfidf(self):
      import sklearn.datasets
      import re
      import nlpaug.model.word_stats as nmw
  
      def _tokenizer(text, token_pattern=r"(?u)\b\w\w+\b"):
          token_pattern = re.compile(token_pattern)
          return token_pattern.findall(text)
  
      # Load sample data
      train_data = sklearn.datasets.fetch_20newsgroups(
          subset="train", remove=("headers", "footers", "quotes")
      )
      train_x = train_data.data
  
      # Tokenize input
      train_x_tokens = [_tokenizer(x) for x in train_x]
  
      # Train TF-IDF model
      if not os.path.exists(self.tfidf_model_path):
          os.makedirs(self.tfidf_model_path)
  
      tfidf_model = nmw.TfIdf()
      tfidf_model.train(train_x_tokens)
      tfidf_model.save(self.tfidf_model_path)
  
file_path: nlpaug/test/augmenter/word/test_word.py
imports: import unittest
  import os
  from dotenv import load_dotenv
  import nlpaug.augmenter.word
  from nlpaug.util import Action, Doc
label_desc: Use image node_id 3 for calling the TestWord obj's underlying member method code with example usage: obj._train_tfidf() and returns: token_pattern